Overview

Dataset statistics

Number of variables10
Number of observations682
Missing cells0
Missing cells (%)0.0%
Duplicate rows46
Duplicate rows (%)6.7%
Total size in memory74.8 KiB
Average record size in memory112.3 B

Variable types

Numeric9
Categorical1

Alerts

Dataset has 46 (6.7%) duplicate rowsDuplicates
Clump_Thickness is highly overall correlated with Uniformity_of_Cell_Size and 7 other fieldsHigh correlation
Uniformity_of_Cell_Size is highly overall correlated with Clump_Thickness and 8 other fieldsHigh correlation
Uniformity_of_Cell_Shape is highly overall correlated with Clump_Thickness and 7 other fieldsHigh correlation
Marginal_Adhesion is highly overall correlated with Clump_Thickness and 7 other fieldsHigh correlation
Single_Epithelial_Cell_Size is highly overall correlated with Clump_Thickness and 7 other fieldsHigh correlation
Bare_Nuclei is highly overall correlated with Clump_Thickness and 7 other fieldsHigh correlation
Bland_Chromatin is highly overall correlated with Clump_Thickness and 7 other fieldsHigh correlation
Normal_Nucleoli is highly overall correlated with Clump_Thickness and 8 other fieldsHigh correlation
Mitoses is highly overall correlated with Uniformity_of_Cell_Size and 2 other fieldsHigh correlation
Class is highly overall correlated with Clump_Thickness and 8 other fieldsHigh correlation

Reproduction

Analysis started2023-02-19 23:51:59.892629
Analysis finished2023-02-19 23:52:07.886807
Duration7.99 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Clump_Thickness
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.441349
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:07.939673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8227505
Coefficient of variation (CV)0.63556153
Kurtosis-0.63564731
Mean4.441349
Median Absolute Deviation (MAD)2
Skewness0.58813685
Sum3029
Variance7.9679206
MonotonicityNot monotonic
2023-02-19T17:52:08.014261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 139
20.4%
5 127
18.6%
3 104
15.2%
4 79
11.6%
10 69
10.1%
2 50
 
7.3%
8 44
 
6.5%
6 33
 
4.8%
7 23
 
3.4%
9 14
 
2.1%
ValueCountFrequency (%)
1 139
20.4%
2 50
 
7.3%
3 104
15.2%
4 79
11.6%
5 127
18.6%
6 33
 
4.8%
7 23
 
3.4%
8 44
 
6.5%
9 14
 
2.1%
10 69
10.1%
ValueCountFrequency (%)
10 69
10.1%
9 14
 
2.1%
8 44
 
6.5%
7 23
 
3.4%
6 33
 
4.8%
5 127
18.6%
4 79
11.6%
3 104
15.2%
2 50
 
7.3%
1 139
20.4%

Uniformity_of_Cell_Size
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1539589
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:08.094913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0662854
Coefficient of variation (CV)0.97220207
Kurtosis0.068204082
Mean3.1539589
Median Absolute Deviation (MAD)0
Skewness1.2242539
Sum2151
Variance9.4021062
MonotonicityNot monotonic
2023-02-19T17:52:08.169391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 372
54.5%
10 67
 
9.8%
3 52
 
7.6%
2 45
 
6.6%
4 38
 
5.6%
5 30
 
4.4%
8 28
 
4.1%
6 25
 
3.7%
7 19
 
2.8%
9 6
 
0.9%
ValueCountFrequency (%)
1 372
54.5%
2 45
 
6.6%
3 52
 
7.6%
4 38
 
5.6%
5 30
 
4.4%
6 25
 
3.7%
7 19
 
2.8%
8 28
 
4.1%
9 6
 
0.9%
10 67
 
9.8%
ValueCountFrequency (%)
10 67
 
9.8%
9 6
 
0.9%
8 28
 
4.1%
7 19
 
2.8%
6 25
 
3.7%
5 30
 
4.4%
4 38
 
5.6%
3 52
 
7.6%
2 45
 
6.6%
1 372
54.5%

Uniformity_of_Cell_Shape
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2184751
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:08.249669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9895676
Coefficient of variation (CV)0.92887703
Kurtosis-0.021862589
Mean3.2184751
Median Absolute Deviation (MAD)0
Skewness1.1557776
Sum2195
Variance8.9375143
MonotonicityNot monotonic
2023-02-19T17:52:08.323331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 345
50.6%
10 58
 
8.5%
2 58
 
8.5%
3 53
 
7.8%
4 43
 
6.3%
5 32
 
4.7%
7 30
 
4.4%
6 29
 
4.3%
8 27
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 345
50.6%
2 58
 
8.5%
3 53
 
7.8%
4 43
 
6.3%
5 32
 
4.7%
6 29
 
4.3%
7 30
 
4.4%
8 27
 
4.0%
9 7
 
1.0%
10 58
 
8.5%
ValueCountFrequency (%)
10 58
 
8.5%
9 7
 
1.0%
8 27
 
4.0%
7 30
 
4.4%
6 29
 
4.3%
5 32
 
4.7%
4 43
 
6.3%
3 53
 
7.8%
2 58
 
8.5%
1 345
50.6%

Marginal_Adhesion
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8328446
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:08.403903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8658054
Coefficient of variation (CV)1.0116352
Kurtosis0.93544847
Mean2.8328446
Median Absolute Deviation (MAD)0
Skewness1.5070038
Sum1932
Variance8.2128404
MonotonicityNot monotonic
2023-02-19T17:52:08.476376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 392
57.5%
3 58
 
8.5%
2 58
 
8.5%
10 55
 
8.1%
4 33
 
4.8%
8 25
 
3.7%
5 23
 
3.4%
6 21
 
3.1%
7 13
 
1.9%
9 4
 
0.6%
ValueCountFrequency (%)
1 392
57.5%
2 58
 
8.5%
3 58
 
8.5%
4 33
 
4.8%
5 23
 
3.4%
6 21
 
3.1%
7 13
 
1.9%
8 25
 
3.7%
9 4
 
0.6%
10 55
 
8.1%
ValueCountFrequency (%)
10 55
 
8.1%
9 4
 
0.6%
8 25
 
3.7%
7 13
 
1.9%
6 21
 
3.1%
5 23
 
3.4%
4 33
 
4.8%
3 58
 
8.5%
2 58
 
8.5%
1 392
57.5%
Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2360704
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:08.553905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2242135
Coefficient of variation (CV)0.68731927
Kurtosis2.1210827
Mean3.2360704
Median Absolute Deviation (MAD)0
Skewness1.7014343
Sum2207
Variance4.9471258
MonotonicityNot monotonic
2023-02-19T17:52:08.628982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 375
55.0%
3 71
 
10.4%
4 48
 
7.0%
1 44
 
6.5%
6 40
 
5.9%
5 39
 
5.7%
10 31
 
4.5%
8 21
 
3.1%
7 11
 
1.6%
9 2
 
0.3%
ValueCountFrequency (%)
1 44
 
6.5%
2 375
55.0%
3 71
 
10.4%
4 48
 
7.0%
5 39
 
5.7%
6 40
 
5.9%
7 11
 
1.6%
8 21
 
3.1%
9 2
 
0.3%
10 31
 
4.5%
ValueCountFrequency (%)
10 31
 
4.5%
9 2
 
0.3%
8 21
 
3.1%
7 11
 
1.6%
6 40
 
5.9%
5 39
 
5.7%
4 48
 
7.0%
3 71
 
10.4%
2 375
55.0%
1 44
 
6.5%

Bare_Nuclei
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5483871
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:08.711406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6452256
Coefficient of variation (CV)1.0272909
Kurtosis-0.80333788
Mean3.5483871
Median Absolute Deviation (MAD)0
Skewness0.98777871
Sum2420
Variance13.28767
MonotonicityNot monotonic
2023-02-19T17:52:08.783945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 401
58.8%
10 132
 
19.4%
2 30
 
4.4%
5 30
 
4.4%
3 28
 
4.1%
8 21
 
3.1%
4 19
 
2.8%
9 9
 
1.3%
7 8
 
1.2%
6 4
 
0.6%
ValueCountFrequency (%)
1 401
58.8%
2 30
 
4.4%
3 28
 
4.1%
4 19
 
2.8%
5 30
 
4.4%
6 4
 
0.6%
7 8
 
1.2%
8 21
 
3.1%
9 9
 
1.3%
10 132
 
19.4%
ValueCountFrequency (%)
10 132
 
19.4%
9 9
 
1.3%
8 21
 
3.1%
7 8
 
1.2%
6 4
 
0.6%
5 30
 
4.4%
4 19
 
2.8%
3 28
 
4.1%
2 30
 
4.4%
1 401
58.8%

Bland_Chromatin
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4457478
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:08.862672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4514351
Coefficient of variation (CV)0.71143776
Kurtosis0.16213744
Mean3.4457478
Median Absolute Deviation (MAD)1
Skewness1.0937593
Sum2350
Variance6.009534
MonotonicityNot monotonic
2023-02-19T17:52:08.938661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 160
23.5%
2 160
23.5%
1 150
22.0%
7 71
10.4%
4 39
 
5.7%
5 34
 
5.0%
8 28
 
4.1%
10 20
 
2.9%
9 11
 
1.6%
6 9
 
1.3%
ValueCountFrequency (%)
1 150
22.0%
2 160
23.5%
3 160
23.5%
4 39
 
5.7%
5 34
 
5.0%
6 9
 
1.3%
7 71
10.4%
8 28
 
4.1%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.1%
7 71
10.4%
6 9
 
1.3%
5 34
 
5.0%
4 39
 
5.7%
3 160
23.5%
2 160
23.5%
1 150
22.0%

Normal_Nucleoli
Real number (ℝ)

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.872434
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:09.126228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0540654
Coefficient of variation (CV)1.0632326
Kurtosis0.46700496
Mean2.872434
Median Absolute Deviation (MAD)0
Skewness1.4182061
Sum1959
Variance9.3273154
MonotonicityNot monotonic
2023-02-19T17:52:09.198438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 431
63.2%
10 60
 
8.8%
3 42
 
6.2%
2 36
 
5.3%
8 23
 
3.4%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
7 16
 
2.3%
9 15
 
2.2%
ValueCountFrequency (%)
1 431
63.2%
2 36
 
5.3%
3 42
 
6.2%
4 18
 
2.6%
5 19
 
2.8%
6 22
 
3.2%
7 16
 
2.3%
8 23
 
3.4%
9 15
 
2.2%
10 60
 
8.8%
ValueCountFrequency (%)
10 60
 
8.8%
9 15
 
2.2%
8 23
 
3.4%
7 16
 
2.3%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
3 42
 
6.2%
2 36
 
5.3%
1 431
63.2%

Mitoses
Real number (ℝ)

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6041056
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-02-19T17:52:09.273993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7337915
Coefficient of variation (CV)1.0808463
Kurtosis12.249347
Mean1.6041056
Median Absolute Deviation (MAD)0
Skewness3.5083801
Sum1094
Variance3.006033
MonotonicityNot monotonic
2023-02-19T17:52:09.345457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 562
82.4%
2 35
 
5.1%
3 33
 
4.8%
10 14
 
2.1%
4 12
 
1.8%
7 9
 
1.3%
8 8
 
1.2%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 562
82.4%
2 35
 
5.1%
3 33
 
4.8%
4 12
 
1.8%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.2%
10 14
 
2.1%
ValueCountFrequency (%)
10 14
 
2.1%
8 8
 
1.2%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.8%
3 33
 
4.8%
2 35
 
5.1%
1 562
82.4%

Class
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size44.0 KiB
0
443 
1
239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters682
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 443
65.0%
1 239
35.0%

Length

2023-02-19T17:52:09.430492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-19T17:52:09.528592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 443
65.0%
1 239
35.0%

Most occurring characters

ValueCountFrequency (%)
0 443
65.0%
1 239
35.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 682
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 443
65.0%
1 239
35.0%

Most occurring scripts

ValueCountFrequency (%)
Common 682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 443
65.0%
1 239
35.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 443
65.0%
1 239
35.0%

Interactions

2023-02-19T17:52:06.848103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:00.562640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.339826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.174997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.945587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.711911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.476835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.231302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.086740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.930631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:00.651963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.425333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.262890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.033262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.797250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.562168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.313384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.171310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:07.016156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:00.737114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.569490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.347006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.118511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.880628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.646040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.396861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.256436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:07.098874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:00.820733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.652589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.431792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.205338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.966790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.731479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.580895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.340083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:07.182471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:00.907298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.736888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.516886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.289023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.052405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.815438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.663045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.426354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:07.266498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:00.998588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.821048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.602661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.376103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.138311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.901278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.750725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.510408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:07.348732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.091420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.905618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.685745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.463743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.221651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.983769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.832515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.595467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:07.435680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.173544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.990389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.769790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.548542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.306824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.066887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.917550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.680284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:07.521367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:01.256149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.089623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:02.860987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:03.630686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:04.391028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:05.148278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.001684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-19T17:52:06.763907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-19T17:52:09.599696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Clump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
Clump_Thickness1.0000.6650.6680.5450.5870.5920.5340.5670.4210.736
Uniformity_of_Cell_Size0.6651.0000.8950.7450.7930.7690.7210.7520.5130.882
Uniformity_of_Cell_Shape0.6680.8951.0000.7180.7650.7530.6960.7240.4780.868
Marginal_Adhesion0.5450.7450.7181.0000.6650.6970.6290.6360.4470.747
Single_Epithelial_Cell_Size0.5870.7930.7650.6651.0000.6940.6450.7110.4830.802
Bare_Nuclei0.5920.7690.7530.6970.6941.0000.6790.6590.4740.839
Bland_Chromatin0.5340.7210.6960.6290.6450.6791.0000.6630.3910.807
Normal_Nucleoli0.5670.7520.7240.6360.7110.6590.6631.0000.5100.773
Mitoses0.4210.5130.4780.4470.4830.4740.3910.5101.0000.519
Class0.7360.8820.8680.7470.8020.8390.8070.7730.5191.000

Missing values

2023-02-19T17:52:07.643403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-19T17:52:07.803143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Clump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
054457103210
13111223110
26881343710
34113213110
48101087109711
511112103110
62121213110
72111211150
84211212110
91111113110
Clump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
6881111211180
6891113211110
690510105454411
6913111211110
6923111212120
6933111321110
6942111211110
6955101037381021
69648643410611
69748854510411

Duplicate rows

Most frequently occurring

Clump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass# duplicates
3111121111027
5111121311023
4111121211021
19311121211020
18311121111012
12211121111010
20311121311010
26411121111010
27411121211010
35511121211010